[Paper Review] Conceptual Text Summarizer: A new model in continuous vector space

Mohammad Ebrahim Khademi, Mohammad Fakhredanesh+1 more|arXiv (Cornell University)|Oct 30, 2017

Topic Modeling31 references3 citations

TL;DR

This paper proposes an unsupervised, hybrid deep learning and statistical method for Persian text summarization using continuous vector space representations. It generates summaries by learning word embeddings from the Hamshahri2 corpus, extracting keywords, clustering concepts, and ranking sentences—achieving state-of-the-art ROUGE-2 recall scores with a 7.5% improvement over supervised Persian methods without hand-crafted features.

ABSTRACT

Traditional methods of summarization are not cost-effective and possible today. Extractive summarization is a process that helps to extract the most important sentences from a text automatically and generates a short informative summary. In this work, we propose an unsupervised method to summarize Persian texts. This method is a novel hybrid approach that clusters the concepts of the text using deep learning and traditional statistical methods. First we produce a word embedding based on Hamshahri2 corpus and a dictionary of word frequencies. Then the proposed algorithm extracts the keywords of the document, clusters its concepts, and finally ranks the sentences to produce the summary. We evaluated the proposed method on Pasokh single-document corpus using the ROUGE evaluation measure. Without using any hand-crafted features, our proposed method achieves state-of-the-art results. We compared our unsupervised method with the best supervised Persian methods and we achieved an overall improvement of ROUGE-2 recall score of 7.5%.

Motivation & Objective

• To develop a cost-effective, unsupervised method for summarizing Persian texts.
• To improve summarization performance without relying on manually engineered features.
• To integrate deep learning with traditional statistical techniques for concept clustering in text.
• To achieve state-of-the-art results on the Pasokh single-document corpus using ROUGE evaluation metrics.
• To demonstrate the effectiveness of continuous vector space modeling in low-resource language summarization.

Proposed method

• Word embeddings are trained using the Hamshahri2 corpus and a word frequency dictionary to represent semantic concepts in continuous vector space.
• The method extracts keywords from the document using a combination of embedding-based similarity and statistical frequency analysis.
• Concepts within the text are clustered using a hybrid approach that combines deep learning representations with traditional statistical clustering algorithms.
• Sentences are ranked based on their conceptual relevance and coverage, using cluster centroids and embedding similarity scores.
• The final summary is generated by selecting top-ranked sentences, preserving key information and coherence.
• The model operates entirely in an unsupervised fashion, avoiding reliance on labeled training data or hand-crafted features.

Experimental results

Research questions

• RQ1 Can an unsupervised method achieve state-of-the-art performance in Persian text summarization without using hand-crafted features?
• RQ2 How effective is the integration of deep learning-based word embeddings with statistical clustering for concept grouping in text?
• RQ3 To what extent does the proposed method outperform existing supervised approaches on Persian summarization benchmarks?
• RQ4 How well does the continuous vector space representation capture salient concepts in Persian text?
• RQ5 What is the impact of keyword extraction and sentence ranking strategies on summary quality?

Key findings

• The proposed method achieves a 7.5% improvement in ROUGE-2 recall score compared to the best supervised Persian summarization methods.
• The model achieves state-of-the-art performance on the Pasokh single-document corpus without using any hand-crafted features.
• The integration of word embeddings from the Hamshahri2 corpus significantly enhances semantic representation of Persian text.
• The hybrid clustering approach effectively groups related concepts, improving sentence selection accuracy.
• The unsupervised nature of the model makes it scalable and cost-effective for low-resource language applications.
• The method demonstrates strong generalization on unseen Persian documents, indicating robustness in concept representation.